# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Define SwinTransformer model"""
import mindspore.common.initializer as weight_init
import mindspore.ops.operations as P
import numpy as np
from mindspore import Parameter, Tensor
from mindspore import dtype as mstype
from mindspore import nn, numpy, ops

from src.models.swintransformer.misc import DropPath1D, Identity, _ntuple

to_2tuple = _ntuple(2)

act_layers = {
    "GELU": nn.GELU,
    "gelu": nn.GELU,
}


class Mlp(nn.Cell):
    """MLP Cell"""

    def __init__(
        self,
        in_features,
        hidden_features=None,
        out_features=None,
        act_layer=act_layers["GELU"],
        drop=0.0,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Dense(
            in_channels=in_features, out_channels=hidden_features, has_bias=True
        )
        self.act = act_layer()
        self.fc2 = nn.Dense(
            in_channels=hidden_features, out_channels=out_features, has_bias=True
        )
        self.drop = nn.Dropout(keep_prob=1.0 - drop)

    def construct(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


def window_partition(x, window_size):
    """
    Args:
        x: (B, H, W, C)
        window_size (int): window size

    Returns:
        windows: (num_windows*B, window_size, window_size, C)
    """
    B, H, W, C = x.shape
    x = np.reshape(
        x, (B, H // window_size, window_size, W // window_size, window_size, C)
    )
    windows = x.transpose(0, 1, 3, 2, 4, 5).reshape(-1, window_size, window_size, C)
    return windows


class WindowPartitionConstruct(nn.Cell):
    """WindowPartitionConstruct Cell"""

    def __init__(self, window_size):
        super(WindowPartitionConstruct, self).__init__()

        self.window_size = window_size

    def construct(self, x):
        """
        Args:
            x: (B, H, W, C)
            window_size (int): window size

        Returns:
            windows: (num_windows*B, window_size, window_size, C)
        """
        B, H, W, C = x.shape
        x = P.Reshape()(
            x,
            (
                B,
                H // self.window_size,
                self.window_size,
                W // self.window_size,
                self.window_size,
                C,
            ),
        )
        x = P.Transpose()(x, (0, 1, 3, 2, 4, 5))
        x = P.Reshape()(
            x,
            (
                B * H * W // (self.window_size ** 2),
                self.window_size,
                self.window_size,
                C,
            ),
        )

        return x


class WindowReverseConstruct(nn.Cell):
    """WindowReverseConstruct Cell"""

    def construct(self, windows, window_size, H, W):
        """
        Args:
            windows: (num_windows*B, window_size, window_size, C)
            window_size (int): Window size
            H (int): Height of image
            W (int): Width of image

        Returns:
            x: (B, H, W, C)
        """
        B = windows.shape[0] // (H * W // window_size // window_size)
        x = ops.Reshape()(
            windows,
            (B, H // window_size, W // window_size, window_size, window_size, -1),
        )
        x = ops.Transpose()(x, (0, 1, 3, 2, 4, 5))
        x = ops.Reshape()(x, (B, H, W, -1))
        return x


class WindowAttention(nn.Cell):
    r"""Window based multi-head self attention (W-MSA) Cell with relative position bias.
    It supports both of shifted and non-shifted window.

    Args:
        dim (int): Number of input channels.
        window_size (tuple[int]): The height and width of the window.
        num_heads (int): Number of attention heads.
        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
        qZk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
    """

    def __init__(
        self,
        dim,
        window_size,
        num_heads,
        qkv_bias=True,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
    ):

        super().__init__()
        if isinstance(dim, tuple) and len(dim) == 1:
            dim = dim[0]
        self.dim = dim
        self.window_size = window_size  # Wh, Ww
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = Tensor(qk_scale or head_dim ** -0.5, mstype.float32)
        self.relative_bias = RelativeBias(self.window_size, num_heads)

        # get pair-wise relative position index for each token inside the window
        self.q = nn.Dense(in_channels=dim, out_channels=dim, has_bias=qkv_bias)
        self.k = nn.Dense(in_channels=dim, out_channels=dim, has_bias=qkv_bias)
        self.v = nn.Dense(in_channels=dim, out_channels=dim, has_bias=qkv_bias)

        self.attn_drop = nn.Dropout(keep_prob=1.0 - attn_drop)
        self.proj = nn.Dense(in_channels=dim, out_channels=dim, has_bias=True)
        self.proj_drop = nn.Dropout(keep_prob=1.0 - proj_drop)
        self.softmax = nn.Softmax(axis=-1)

    def construct(self, x, mask=None):
        """
        Args:
            x: input features with shape of (num_windows*B, N, C)
            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
        """
        B_, N, C = x.shape
        q = (
            ops.Reshape()(self.q(x), (B_, N, self.num_heads, C // self.num_heads))
            * self.scale
        )
        q = ops.Transpose()(q, (0, 2, 1, 3))
        k = ops.Reshape()(self.k(x), (B_, N, self.num_heads, C // self.num_heads))
        k = ops.Transpose()(k, (0, 2, 3, 1))
        v = ops.Reshape()(self.v(x), (B_, N, self.num_heads, C // self.num_heads))
        v = ops.Transpose()(v, (0, 2, 1, 3))

        attn = ops.BatchMatMul()(q, k)
        attn = attn + self.relative_bias()

        if mask is not None:
            nW = mask.shape[1]
            attn = (
                P.Reshape()(
                    attn,
                    (
                        B_ // nW,
                        nW,
                        self.num_heads,
                        N,
                        N,
                    ),
                )
                + mask
            )
            attn = P.Reshape()(
                attn,
                (
                    -1,
                    self.num_heads,
                    N,
                    N,
                ),
            )
            attn = self.softmax(attn)
        else:
            attn = self.softmax(attn)
        attn = self.attn_drop(attn)
        x = ops.Reshape()(
            ops.Transpose()(ops.BatchMatMul()(attn, v), (0, 2, 1, 3)), (B_, N, C)
        )
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}"


class RelativeBias(nn.Cell):
    """RelativeBias Cell"""

    def __init__(self, window_size, num_heads):
        super(RelativeBias, self).__init__()
        self.window_size = window_size
        # define a parameter table of relative position bias
        coords_h = (
            np.arange(self.window_size[0])
            .reshape(self.window_size[0], 1)
            .repeat(self.window_size[0], 1)
            .reshape(1, -1)
        )
        coords_w = (
            np.arange(self.window_size[1])
            .reshape(1, self.window_size[1])
            .repeat(self.window_size[1], 0)
            .reshape(1, -1)
        )
        coords_flatten = np.concatenate([coords_h, coords_w], axis=0)  # 2, Wh, Ww
        relative_coords = (
            coords_flatten[:, :, np.newaxis] - coords_flatten[:, np.newaxis, :]
        )  # 2, Wh*Ww, Wh*Ww
        relative_coords = relative_coords.transpose(1, 2, 0)  # Wh*Ww, Wh*Ww, 2
        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
        relative_coords[:, :, 1] += self.window_size[1] - 1
        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
        self.relative_position_index = Tensor(
            relative_coords.sum(-1).reshape(-1)
        )  # Wh*Ww, Wh*Ww
        self.relative_position_bias_table = Parameter(
            Tensor(
                np.random.randn(
                    (2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads
                ),
                dtype=mstype.float32,
            )
        )  # 2*Wh-1 * 2*Ww-1, nH
        self.one_hot = nn.OneHot(
            axis=-1,
            depth=(2 * window_size[0] - 1) * (2 * window_size[1] - 1),
            dtype=mstype.float32,
        )
        self.index = Parameter(
            self.one_hot(self.relative_position_index), requires_grad=False
        )

    def construct(self, axis=0):
        out = ops.MatMul()(self.index, self.relative_position_bias_table)
        out = P.Reshape()(
            out,
            (
                self.window_size[0] * self.window_size[1],
                self.window_size[0] * self.window_size[1],
                -1,
            ),
        )
        out = P.Transpose()(out, (2, 0, 1))
        out = ops.ExpandDims()(out, 0)
        return out


class SwinTransformerBlock(nn.Cell):
    """Swin Transformer Block.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        num_heads (int): Number of attention heads.
        window_size (int): Window size.
        shift_size (int): Shift size for SW-MSA.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float, optional): Stochastic depth rate. Default: 0.0
        act_layer (nn.Cell, optional): Activation layer. Default: nn.GELU
        norm_layer (nn.Cell, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(
        self,
        dim,
        input_resolution,
        num_heads,
        window_size=7,
        shift_size=0,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        act_layer=act_layers["GELU"],
        norm_layer=nn.LayerNorm,
    ):
        super(SwinTransformerBlock, self).__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        if min(self.input_resolution) <= self.window_size:
            # if window size is larger than input resolution, we don't partition windows
            self.shift_size = 0
            self.window_size = min(self.input_resolution)

        if isinstance(dim, int):
            dim = (dim,)

        self.norm1 = norm_layer(dim, epsilon=1e-5)
        self.attn = WindowAttention(
            dim,
            window_size=to_2tuple(self.window_size),
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=drop,
        )

        self.drop_path = DropPath1D(drop_path) if drop_path > 0.0 else Identity()
        self.norm2 = norm_layer(dim, epsilon=1e-5)
        mlp_hidden_dim = int((dim[0] if isinstance(dim, tuple) else dim) * mlp_ratio)
        self.mlp = Mlp(
            in_features=dim[0] if isinstance(dim, tuple) else dim,
            hidden_features=mlp_hidden_dim,
            act_layer=act_layer,
            drop=drop,
        )
        if self.shift_size > 0:
            # calculate attention mask for SW-MSA
            H, W = self.input_resolution
            img_mask = np.zeros((1, H, W, 1))  # 1 H W 1
            h_slices = (
                slice(0, -self.window_size),
                slice(-self.window_size, -self.shift_size),
                slice(-self.shift_size, None),
            )
            w_slices = (
                slice(0, -self.window_size),
                slice(-self.window_size, -self.shift_size),
                slice(-self.shift_size, None),
            )
            cnt = 0
            for h in h_slices:
                for w in w_slices:
                    img_mask[:, h, w, :] = cnt
                    cnt += 1
            # img_mask: [1, 56, 56, 1] window_size: 7
            mask_windows = window_partition(
                img_mask, self.window_size
            )  # nW, window_size, window_size, 1
            mask_windows = mask_windows.reshape(-1, self.window_size * self.window_size)
            attn_mask = mask_windows[:, np.newaxis] - mask_windows[:, :, np.newaxis]
            # [64, 49, 49] ==> [1, 64, 1, 49, 49]
            attn_mask = np.expand_dims(attn_mask, axis=1)
            attn_mask = np.expand_dims(attn_mask, axis=0)
            attn_mask = Tensor(
                np.where(attn_mask == 0, 0.0, -100.0), dtype=mstype.float32
            )
            self.attn_mask = Parameter(attn_mask, requires_grad=False)
            self.roll_pos = Roll(self.shift_size)
            self.roll_neg = Roll(-self.shift_size)
        else:
            self.attn_mask = None

        self.window_partition = WindowPartitionConstruct(self.window_size)
        self.window_reverse = WindowReverseConstruct()

    def construct(self, x):
        """construct function"""
        H, W = self.input_resolution
        B, _, C = x.shape

        shortcut = x
        x = self.norm1(x)
        x = P.Reshape()(
            x,
            (
                B,
                H,
                W,
                C,
            ),
        )

        # cyclic shift
        if self.shift_size > 0:
            shifted_x = self.roll_neg(x)
            # shifted_x = numpy.roll(x, (-self.shift_size, -self.shift_size), (1, 2))
        else:
            shifted_x = x

        # partition windows
        x_windows = self.window_partition(
            shifted_x
        )  # nW*B, window_size, window_size, C
        x_windows = ops.Reshape()(
            x_windows,
            (
                -1,
                self.window_size * self.window_size,
                C,
            ),
        )  # nW*B, window_size*window_size, C

        # W-MSA/SW-MSA
        attn_windows = self.attn(
            x_windows, mask=self.attn_mask
        )  # nW*B, window_size*window_size, C

        # merge windows
        attn_windows = P.Reshape()(
            attn_windows,
            (
                -1,
                self.window_size,
                self.window_size,
                C,
            ),
        )
        shifted_x = self.window_reverse(
            attn_windows, self.window_size, H, W
        )  # B H' W' C

        # reverse cyclic shift
        if self.shift_size > 0:
            x = self.roll_pos(shifted_x)
            # x = numpy.roll(shifted_x, (self.shift_size, self.shift_size), (1, 2))  # TODO:Don't stupid
        else:
            x = shifted_x

        x = P.Reshape()(
            x,
            (
                B,
                H * W,
                C,
            ),
        )

        # FFN
        x = shortcut + self.drop_path(x)

        x = x + self.drop_path(self.mlp(self.norm2(x)))

        return x

    def extra_repr(self) -> str:
        return (
            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
            f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
        )


class Roll(nn.Cell):
    """Roll Cell"""

    def __init__(self, shift_size, shift_axis=(1, 2)):
        super(Roll, self).__init__()
        self.shift_size = to_2tuple(shift_size)
        self.shift_axis = shift_axis

    def construct(self, x):
        x = numpy.roll(x, self.shift_size, self.shift_axis)
        return x


class PatchMerging(nn.Cell):
    """Patch Merging Layer.

    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.input_resolution = input_resolution
        self.dim = dim[0] if isinstance(dim, tuple) and len(dim) == 1 else dim
        # Default False
        self.reduction = nn.Dense(
            in_channels=4 * dim, out_channels=2 * dim, has_bias=False
        )
        self.norm = norm_layer(
            [
                dim * 4,
            ]
        )
        self.H, self.W = self.input_resolution
        self.H_2, self.W_2 = self.H // 2, self.W // 2
        self.H2W2 = int(self.H * self.W // 4)
        self.dim_mul_4 = int(dim * 4)
        self.H2W2 = int(self.H * self.W // 4)

    def construct(self, x):
        """
        x: B, H*W, C
        """
        B = x.shape[0]
        x = P.Reshape()(x, (B, self.H_2, 2, self.W_2, 2, self.dim))
        x = P.Transpose()(x, (0, 1, 3, 4, 2, 5))
        x = P.Reshape()(x, (B, self.H2W2, self.dim_mul_4))
        x = self.norm(x)
        x = self.reduction(x)

        return x

    def extra_repr(self) -> str:
        return f"input_resolution={self.input_resolution}, dim={self.dim}"


class BasicLayer(nn.Cell):
    """A basic Swin Transformer layer for one stage.

    Args:
        dim (int): Number of input channels.
        input_resolution (tuple[int]): Input resolution.
        depth (int): Number of blocks.
        num_heads (int): Number of attention heads.
        window_size (int): Local window size.
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
        drop (float, optional): Dropout rate. Default: 0.0
        attn_drop (float, optional): Attention dropout rate. Default: 0.0
        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
        norm_layer (nn.Cell, optional): Normalization layer. Default: nn.LayerNorm
        downsample (nn.Cell | None, optional): Downsample layer at the end of the layer. Default: None
    """

    def __init__(
        self,
        dim,
        input_resolution,
        depth,
        num_heads,
        window_size,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop=0.0,
        attn_drop=0.0,
        drop_path=0.0,
        norm_layer=nn.LayerNorm,
        downsample=None,
    ):

        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth

        # build blocks
        self.blocks = nn.CellList(
            [
                SwinTransformerBlock(
                    dim=dim,
                    input_resolution=input_resolution,
                    num_heads=num_heads,
                    window_size=window_size,
                    shift_size=0
                    if (i % 2 == 0)
                    else window_size // 2,  # TODO: 这里window_size//2的时候特别慢
                    mlp_ratio=mlp_ratio,
                    qkv_bias=qkv_bias,
                    qk_scale=qk_scale,
                    drop=drop,
                    attn_drop=attn_drop,
                    drop_path=drop_path[i]
                    if isinstance(drop_path, list)
                    else drop_path,
                    norm_layer=norm_layer,
                )
                for i in range(depth)
            ]
        )

        # patch merging layer
        if downsample is not None:
            self.downsample = downsample(
                input_resolution, dim=dim, norm_layer=norm_layer
            )
        else:
            self.downsample = None

    def construct(self, x):
        """construct"""
        for blk in self.blocks:
            x = blk(x)
        if self.downsample is not None:
            x = self.downsample(x)
        return x

    def extra_repr(self) -> str:
        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"


class PatchEmbed(nn.Cell):
    """Image to Patch Embedding

    Args:
        image_size (int): Image size.  Default: 224.
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Cell, optional): Normalization layer. Default: None
    """

    def __init__(
        self, image_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
    ):
        super().__init__()
        image_size = to_2tuple(image_size)
        patch_size = to_2tuple(patch_size)
        patches_resolution = [
            image_size[0] // patch_size[0],
            image_size[1] // patch_size[1],
        ]
        self.image_size = image_size
        self.patch_size = patch_size
        self.patches_resolution = patches_resolution
        self.num_patches = patches_resolution[0] * patches_resolution[1]

        self.in_chans = in_chans
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(
            in_channels=in_chans,
            out_channels=embed_dim,
            kernel_size=patch_size,
            stride=patch_size,
            pad_mode="pad",
            has_bias=True,
            weight_init="TruncatedNormal",
        )

        if norm_layer is not None:
            if isinstance(embed_dim, int):
                embed_dim = (embed_dim,)
            self.norm = norm_layer(embed_dim, epsilon=1e-5)
        else:
            self.norm = None

    def construct(self, x):
        """docstring"""
        B = x.shape[0]
        # FIXME look at relaxing size constraints
        x = ops.Reshape()(self.proj(x), (B, self.embed_dim, -1))  # B Ph*Pw C
        x = ops.Transpose()(x, (0, 2, 1))

        if self.norm is not None:
            x = self.norm(x)
        return x


class SwinTransformer(nn.Cell):
    """Swin Transformer
        A Pynp impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
          https://arxiv.org/pdf/2103.14030

    Args:
        image_size (int | tuple(int)): Input image size. Default 224
        patch_size (int | tuple(int)): Patch size. Default: 4
        in_chans (int): Number of input image channels. Default: 3
        num_classes (int): Number of classes for classification head. Default: 1000
        embed_dim (int): Patch embedding dimension. Default: 96
        depths (tuple(int)): Depth of each Swin Transformer layer.
        num_heads (tuple(int)): Number of attention heads in different layers.
        window_size (int): Window size. Default: 7
        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
        drop_rate (float): Dropout rate. Default: 0
        attn_drop_rate (float): Attention dropout rate. Default: 0
        drop_path_rate (float): Stochastic depth rate. Default: 0.1
        norm_layer (nn.Cell): Normalization layer. Default: nn.LayerNorm.
        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
        patch_norm (bool): If True, add normalization after patch embedding. Default: True
    """

    def __init__(
        self,
        image_size=224,
        patch_size=4,
        in_chans=3,
        num_classes=1000,
        embed_dim=96,
        depths=None,
        num_heads=None,
        window_size=7,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.1,
        norm_layer=nn.LayerNorm,
        ape=False,
        patch_norm=True,
    ):
        super().__init__()

        self.num_classes = num_classes
        self.num_layers = len(depths)
        self.embed_dim = embed_dim
        self.ape = ape
        self.patch_norm = patch_norm
        self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
        self.mlp_ratio = mlp_ratio

        # split image into non-overlapping patches
        self.patch_embed = PatchEmbed(
            image_size=image_size,
            patch_size=patch_size,
            in_chans=in_chans,
            embed_dim=embed_dim,
            norm_layer=norm_layer if self.patch_norm else None,
        )
        num_patches = self.patch_embed.num_patches
        patches_resolution = self.patch_embed.patches_resolution
        self.patches_resolution = patches_resolution

        # absolute position embedding
        if self.ape:
            self.absolute_pos_embed = Parameter(
                Tensor(np.zeros(1, num_patches, embed_dim), dtype=mstype.float32)
            )

        self.pos_drop = nn.Dropout(keep_prob=1.0 - drop_rate)

        # stochastic depth
        dpr = [
            x for x in np.linspace(0, drop_path_rate, sum(depths))
        ]  # stochastic depth decay rule

        # build layers
        self.layers = nn.CellList()
        for i_layer in range(self.num_layers):
            layer = BasicLayer(
                dim=int(embed_dim * 2 ** i_layer),
                input_resolution=(
                    patches_resolution[0] // (2 ** i_layer),
                    patches_resolution[1] // (2 ** i_layer),
                ),
                depth=depths[i_layer],
                num_heads=num_heads[i_layer],
                window_size=window_size,
                mlp_ratio=self.mlp_ratio,
                qkv_bias=qkv_bias,
                qk_scale=qk_scale,
                drop=drop_rate,
                attn_drop=attn_drop_rate,
                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                norm_layer=norm_layer,
                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
            )
            self.layers.append(layer)

        self.norm = norm_layer(
            [
                self.num_features,
            ],
            epsilon=1e-5,
        )
        self.avgpool = P.ReduceMean(keep_dims=False)
        self.head = (
            nn.Dense(
                in_channels=self.num_features, out_channels=num_classes, has_bias=True
            )
            if num_classes > 0
            else Identity()
        )
        self.init_weights()

    def init_weights(self):
        """init_weights"""
        for _, cell in self.cells_and_names():
            if isinstance(cell, nn.Dense):
                cell.weight.set_data(
                    weight_init.initializer(
                        weight_init.TruncatedNormal(sigma=0.02),
                        cell.weight.shape,
                        cell.weight.dtype,
                    )
                )
                if isinstance(cell, nn.Dense) and cell.bias is not None:
                    cell.bias.set_data(
                        weight_init.initializer(
                            weight_init.Zero(), cell.bias.shape, cell.bias.dtype
                        )
                    )
            elif isinstance(cell, nn.LayerNorm):
                cell.gamma.set_data(
                    weight_init.initializer(
                        weight_init.One(), cell.gamma.shape, cell.gamma.dtype
                    )
                )
                cell.beta.set_data(
                    weight_init.initializer(
                        weight_init.Zero(), cell.beta.shape, cell.beta.dtype
                    )
                )

    def no_weight_decay(self):
        return {"absolute_pos_embed"}

    def no_weight_decay_keywords(self):
        return {"relative_position_bias_table"}

    def forward_features(self, x):
        x = self.patch_embed(x)
        if self.ape:
            x = x + self.absolute_pos_embed
        x = self.pos_drop(x)
        for layer in self.layers:
            x = layer(x)
        x = self.norm(x)  # B L C
        x = self.avgpool(ops.Transpose()(x, (0, 2, 1)), 2)  # B C 1
        return x

    def construct(self, x):
        x = self.forward_features(x)
        x = self.head(x)
        return x
